zenresize

zenresize is a SIMD-accelerated image resampling library with crop, resize, and canvas padding in streaming or fullframe modes.

[dependencies]
zenresize = "0.1"

Quick Start

use zenresize::{Resizer, ResizeConfig, Filter, PixelDescriptor};

let input = vec![128u8; 1024 * 768 * 4]; // RGBA pixels

let config = ResizeConfig::builder(1024, 768, 512, 384)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBA8_SRGB)
    .build();

let output = Resizer::new(&config).resize(&input);
assert_eq!(output.len(), 512 * 384 * 4);

Operations

All operations work in the streaming API. Crop and padding also work independently (without resize) by setting output dimensions equal to crop/content dimensions.

Operation	What it does	Builder method
Resize	Resample to new dimensions with a choice of 31 filters	`.filter(Filter::Lanczos)`
Fit	Aspect-preserving resize to a target box	`.fit(FitMode::Fit, max_w, max_h)`
Crop	Extract a rectangular region from the input	`.crop(x, y, w, h)`
Pad	Add solid-color border around the output	`.padding(top, right, bottom, left)`
Orient	Apply EXIF orientation (rotate/flip) post-resize	`stream.with_orientation(OrientOutput::Rotate90)`
Crop + Resize	Extract region, then resize it	`.crop(...)` on a config with different output dims
Resize + Pad	Resize, then add padding	`.padding(...)` on a config with different input/output dims
Crop + Resize + Pad	All three in sequence	`.crop(...)` + `.padding(...)`

The pipeline order is always: crop (input side) -> resize -> pad (output side).

Features

Crop, resize, and pad -- independently or combined, streaming or fullframe
31 resampling filters (Lanczos, Mitchell, Robidoux, Ginseng, etc.)
sRGB-aware linear-light processing for correct gamma handling
Row-at-a-time streaming API for pipeline integration
Resizer struct for amortizing weight computation across repeated resizes
Alpha premultiply/unpremultiply built into the pipeline
Channel-order-agnostic: RGBA, BGRA, ARGB, BGRX all work without swizzling
u8, u16, and f32 pixel I/O; cross-format resize (e.g., u8 in, f32 out)
no_std + alloc compatible (std optional)
SIMD-accelerated via archmage: AVX2+FMA on x86-64, NEON on ARM, WASM SIMD, scalar fallback
Optional AVX-512 V-filter kernel (avx512 feature)

Resizer

Resizer pre-computes weight tables from the config. Reusing one across images with the same dimensions and filter saves the weight computation cost.

use zenresize::{Resizer, ResizeConfig, Filter, PixelDescriptor};

let config = ResizeConfig::builder(1024, 1024, 512, 512)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBA8_SRGB)
    .build();

let mut resizer = Resizer::new(&config);

// Allocating -- returns a new Vec<u8>
let output: Vec<u8> = resizer.resize(&input);

// Non-allocating -- writes into your buffer
let mut buf = vec![0u8; 512 * 512 * 4];
resizer.resize_into(&input, &mut buf);

For pipelines that already work in linear f32:

let config = ResizeConfig::builder(1024, 1024, 512, 512)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBAF32_LINEAR)
    .build();

let mut resizer = Resizer::new(&config);
let output_f32: Vec<f32> = resizer.resize_f32(&input_f32);

Cross-format resizing (u8 sRGB input, f32 linear output, or any combination):

let mut resizer = Resizer::new(&ResizeConfig::builder(w, h, out_w, out_h)
    .filter(Filter::Lanczos)
    .input(PixelDescriptor::RGBA8_SRGB)
    .output(PixelDescriptor::RGBAF32_LINEAR)
    .build());

let output_f32: Vec<f32> = resizer.resize_u8_to_f32(&input_u8);

StreamingResize

Push input rows one at a time, pull output rows as they become available. Uses a V-first pipeline internally: the H-filter runs only out_height times (once per output row) instead of in_height times.

use zenresize::{StreamingResize, ResizeConfig, Filter, PixelDescriptor};

let config = ResizeConfig::builder(1000, 800, 500, 400)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBA8_SRGB)
    .build();

let mut stream = StreamingResize::new(&config);

for y in 0..800 {
    let row = &input_data[y * 4000..(y + 1) * 4000];
    stream.push_row(row).unwrap();

    // Drain output rows as they become available
    while let Some(out_row) = stream.next_output_row() {
        // out_row is &[u8], width * channels bytes
    }
}
stream.finish();

// Drain remaining output rows
while let Some(out_row) = stream.next_output_row() {
    // ...
}

assert!(stream.is_complete());
assert_eq!(stream.output_rows_produced(), 400);

Zero-copy output

Write output directly into an encoder's buffer:

let row_len = stream.output_row_len();
let mut enc_buf = vec![0u8; row_len];
while stream.next_output_row_into(&mut enc_buf) {
    encoder.write_row(&enc_buf);
}

f32 streaming

stream.push_row_f32(&f32_row).unwrap();

// Or write directly into the resizer's internal buffer (saves a memcpy):
stream.push_row_f32_with(|buf| {
    // fill buf with f32 pixel data
}).unwrap();

while let Some(out_row) = stream.next_output_row_f32() {
    // out_row is &[f32]
}

Compositing

Resize foreground images onto a background in a single pass. Compositing happens in premultiplied linear f32 space between the vertical filter and unpremultiply -- no extra buffer copy.

use zenresize::{StreamingResize, ResizeConfig, Filter, PixelDescriptor, SolidBackground, BlendMode};

let config = ResizeConfig::builder(800, 600, 400, 300)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBA8_SRGB)
    .build();

let bg = SolidBackground::white(PixelDescriptor::RGBA8_SRGB);
let mut stream = StreamingResize::with_background(&config, bg)
    .expect("compositing config")
    .with_blend_mode(BlendMode::SrcOver); // default; 31 modes available

for y in 0..600 {
    stream.push_row(&input[y * 3200..(y + 1) * 3200]).unwrap();
    while let Some(out) = stream.next_output_row() {
        // composited output rows
    }
}

Background types: SolidBackground (constant color), SliceBackground (borrow a buffer), StreamedBackground (push rows), or implement the Background trait yourself. NoBackground (the default) eliminates all composite code at compile time.

Masking

Apply per-pixel masks to control where the foreground is visible. Masks are applied between resize and compositing, so rounded corners over a white background produce white corners (not transparent-over-black).

use zenresize::{StreamingResize, ResizeConfig, PixelDescriptor, SolidBackground, RoundedRectMask};

let config = ResizeConfig::builder(800, 600, 400, 300)
    .format(PixelDescriptor::RGBA8_SRGB)
    .build();

let bg = SolidBackground::white(PixelDescriptor::RGBA8_SRGB);
let mask = RoundedRectMask::new(400, 300, 20.0);
let stream = StreamingResize::with_background(&config, bg)
    .expect("compositing config")
    .with_mask(mask);

Mask types re-exported from zenblend: RoundedRectMask, LinearGradientMask, RadialGradientMask, or implement MaskSource.

Source Region (Crop)

Extract a rectangular region from the input before resizing. The streaming API accepts full-width input rows; the resizer skips rows outside the vertical range and extracts the horizontal region internally.

use zenresize::{StreamingResize, ResizeConfig, Filter, PixelDescriptor};

// Crop a 400x300 region starting at (100, 50), resize to 200x150
let config = ResizeConfig::builder(1000, 800, 200, 150)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBA8_SRGB)
    .crop(100, 50, 400, 300)
    .build();

let mut stream = StreamingResize::new(&config);

// Push full-width rows -- rows outside [50..350) are skipped automatically
for y in 0..800 {
    stream.push_row(&source_rows[y]).unwrap();
    while let Some(out) = stream.next_output_row() {
        // 200 * 4 bytes per row
    }
}

Crop without resize (extract only):

// Extract 400x300 at (100, 50), no resize
let config = ResizeConfig::builder(1000, 800, 400, 300)
    .format(PixelDescriptor::RGBA8_SRGB)
    .crop(100, 50, 400, 300)
    .build();

Fit Modes (Aspect-Ratio Constraints)

Four common ways to fit an input into a target box, preserving aspect ratio where appropriate. One call sets out_width/out_height (and, for Cover, a center-anchored source crop) without reaching for a separate layout crate.

Mode	Behavior	Typical use
`FitMode::Fit`	Aspect-preserving, fit entirely inside bounds. Output `≤` bounds on both axes, `==` on one. May up- or down-scale.	Thumbnail letterbox
`FitMode::Within`	Like `Fit`, but never upscales past input size.	Thumbnails that stay sharp when source is small
`FitMode::Cover`	Aspect-preserving, fills the bounds exactly. Source is center-cropped to target aspect, then resized. Output is exactly `max_w × max_h`.	Hero images, cover art, imageflow `fit=crop`
`FitMode::Stretch`	Ignores aspect, stretches to exact bounds.	Non-photo UI assets

use zenresize::{FitMode, ResizeConfig, Filter, PixelDescriptor};

// 1600×900 source, fit into 800×600 letterbox → 800×450, no crop.
let config = ResizeConfig::builder(1600, 900, 0, 0)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBA8_SRGB)
    .fit(FitMode::Fit, 800, 600)
    .build();
assert_eq!((config.out_width, config.out_height), (800, 450));

// Same source, Cover: center-cropped to 4:3, output exactly 800×600.
let config = ResizeConfig::builder(1600, 900, 0, 0)
    .format(PixelDescriptor::RGBA8_SRGB)
    .fit(FitMode::Cover, 800, 600)
    .build();
assert_eq!((config.out_width, config.out_height), (800, 600));
// `.fit(Cover, ...)` also sets `source_region` for the crop — no extra call.

For raw dimension math without the builder:

use zenresize::{FitMode, fit_dims, fit_cover_source_crop};

// What output dims would FitMode produce?
assert_eq!(fit_dims(1600, 900, 800, 600, FitMode::Fit),   (800, 450));
assert_eq!(fit_dims(1600, 900, 800, 600, FitMode::Cover), (800, 600));
assert_eq!(fit_dims(400, 300,  800, 600, FitMode::Within), (400, 300));

// What source crop does Cover apply?
// Target 4:3 from 16:9 source → crop to 1200×900 centered.
assert_eq!(fit_cover_source_crop(1600, 900, 800, 600), (200, 0, 1200, 900));

The math is a port of zenlayout's fit_inside / crop_to_aspect including snap-to-target rounding — verified byte-identical across a ~6M-case brute-force sweep (tests/vs_zenlayout.rs). Callers migrating from zenlayout for simple fit/within/cover cases see no pixel-level drift.

EXIF Orientation

OrientOutput is the 8-element D4 dihedral group (EXIF orientations 1–8), applied post-resize by the streaming pipeline. If you already hold a zenpixels::Orientation from metadata parsing, it converts directly:

use zenresize::{OrientOutput, Orientation, StreamingResize};

let exif_tag: u8 = 6;  // Rotate 90° CW
let orient = Orientation::from_exif(exif_tag).unwrap_or_default();
let mut resizer = StreamingResize::new(&config).with_orientation(orient.into());

Orientation (re-exported from zenpixels) has the full group algebra — compose, inverse, from_exif, to_exif, swaps_axes — so you can build up composed transforms (e.g. EXIF orient + explicit 180°) and hand the result to zenresize with one .into().

Output Padding

Add a solid-color border around the resized output. The total output becomes (left + width + right) by (top + height + bottom).

use zenresize::{StreamingResize, ResizeConfig, Filter, PixelDescriptor};

// Resize 1000x800 -> 500x400, then add 20px black border
let config = ResizeConfig::builder(1000, 800, 500, 400)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBA8_SRGB)
    .padding_uniform(20)
    .padding_color([0.0, 0.0, 0.0, 1.0])
    .build();

let mut stream = StreamingResize::new(&config);

// output_row_len() is (20 + 500 + 20) * 4 = 2160
// total_output_height() is 20 + 400 + 20 = 440
// Top padding rows are available before any input is pushed

for y in 0..800 {
    stream.push_row(&source_rows[y]).unwrap();
    while let Some(out) = stream.next_output_row() {
        // First 20 rows: solid black
        // Next 400 rows: 20px black + 500px content + 20px black
        // Last 20 rows: solid black
    }
}

Asymmetric letterboxing:

let config = ResizeConfig::builder(1000, 800, 500, 400)
    .format(PixelDescriptor::RGBA8_SRGB)
    .padding(40, 0, 40, 0)              // 40px top/bottom only
    .padding_color([0.0, 0.0, 0.0, 1.0])
    .build();
// Total output: 500 x 480

Padding without resize:

let config = ResizeConfig::builder(500, 400, 500, 400)
    .format(PixelDescriptor::RGBA8_SRGB)
    .padding_uniform(10)
    .padding_color([1.0, 1.0, 1.0, 1.0]) // white border
    .build();
// Total output: 520 x 420

Padding color

The padding_color values are 0.0-1.0 in the output's color space. For sRGB u8 output, 0.5 maps to value 128. For linear f32, 0.5 maps to 0.5. Only the first N channels are used (N = channel count of the output format).

Works with all output types: u8 (next_output_row), f32 (next_output_row_f32), u16 (next_output_row_u16).

Crop + Resize + Pad

All three operations compose naturally:

// Extract 800x600 region, resize to 400x300, add 10px white border
let config = ResizeConfig::builder(2000, 1500, 400, 300)
    .filter(Filter::Lanczos)
    .format(PixelDescriptor::RGBA8_SRGB)
    .crop(200, 100, 800, 600)
    .padding_uniform(10)
    .padding_color([1.0, 1.0, 1.0, 1.0])
    .build();

// Pipeline: crop 800x600 -> resize to 400x300 -> pad to 420x320

ResizeConfig

All resize operations take a ResizeConfig built with the builder pattern.

use zenresize::{ResizeConfig, Filter, PixelDescriptor};

let config = ResizeConfig::builder(in_w, in_h, out_w, out_h)
    .filter(Filter::Lanczos)        // resampling filter (default: Robidoux)
    .format(PixelDescriptor::RGBA8_SRGB)  // sets both input and output format
    .input(PixelDescriptor::RGBA8_SRGB)   // or set them separately
    .output(PixelDescriptor::RGBA8_SRGB)
    .linear()                        // resize in linear light (default)
    .srgb()                          // resize in sRGB space (faster, slight quality loss)
    .resize_sharpen(15.0)            // sharpen during resampling (% negative lobe, default: 0)
    .post_sharpen(0.0)               // post-resize unsharp mask (default: 0.0)
    .crop(x, y, w, h)               // source region (default: full input)
    .padding(top, right, bottom, left)  // output padding (default: none)
    .padding_color([0.0, 0.0, 0.0, 1.0])  // padding fill color
    .in_stride(stride)               // input row stride in elements (default: tightly packed)
    .out_stride(stride)              // output row stride in elements (default: tightly packed)
    .build();

Defaults

If you call .build() with no other methods:

Filter: Robidoux
Format: RGBA8_SRGB for both input and output
Linear: true (sRGB u8 -> linear f32 -> resize -> sRGB u8)
Resize sharpen: 0.0 (natural filter ratio)
Post sharpen: 0.0
Stride: tightly packed (width * channels)

Config fields

ResizeConfig fields are public (#[non_exhaustive]):

config.filter           // Filter
config.in_width         // u32 (full source width)
config.in_height        // u32 (full source height)
config.out_width        // u32 (content output width, before padding)
config.out_height       // u32 (content output height, before padding)
config.input            // PixelDescriptor
config.output           // PixelDescriptor
config.linear           // bool
config.post_sharpen     // f32
config.post_blur_sigma  // f32
config.kernel_width_scale // Option<f64>
config.lobe_ratio       // LobeRatio
config.in_stride        // usize (0 = tightly packed)
config.out_stride       // usize (0 = tightly packed)
config.source_region    // Option<SourceRegion> (crop rectangle)
config.padding          // Option<Padding> (output padding)

Helper methods:

config.resize_in_width()     // crop width if set, else in_width
config.resize_in_height()    // crop height if set, else in_height
config.total_output_width()  // out_width + left + right padding
config.total_output_height() // out_height + top + bottom padding
config.total_output_row_len() // total_output_width * channels

Pixel Formats

PixelDescriptor (from zenpixels) describes pixel format, channel layout, alpha mode, and transfer function in one value.

Supported formats

Format	Channels	Type	Transfer	Constant
RGBA sRGB	4 (straight alpha)	u8	sRGB	`RGBA8_SRGB`
RGBX sRGB	4 (no alpha)	u8	sRGB	`RGBX8_SRGB`
RGB sRGB	3	u8	sRGB	`RGB8_SRGB`
Gray sRGB	1	u8	sRGB	`GRAY8_SRGB`
BGRA sRGB	4 (straight alpha)	u8	sRGB	`BGRA8_SRGB`
RGBA linear	4 (straight alpha)	f32	Linear	`RGBAF32_LINEAR`
RGB linear	3	f32	Linear	`RGBF32_LINEAR`
RGBA sRGB	4 (straight alpha)	u16	sRGB	`RGBA16_SRGB`
RGB sRGB	3	u16	sRGB	`RGB16_SRGB`

Cross-format resize is supported: any input type to any output type (u8 <-> u16 <-> f32).

Transfer functions

All five transfer functions work with all channel types and layouts:

Transfer	Description
`Srgb`	Standard sRGB gamma (default)
`Linear`	Linear light (identity)
`Bt709`	BT.709 broadcast gamma
`Pq`	HDR10 Perceptual Quantizer
`Hlg`	Hybrid Log-Gamma (HDR)

Channel order

Channel order doesn't matter. The sRGB transfer function is the same for R, G, and B, and the convolution kernels operate on N floats per pixel. Pass BGRA data as RGBA8_SRGB -- no swizzling needed. (Use BGRA8_SRGB if you want the descriptor to be semantically accurate, but the resize output is identical either way.)

Color space (`.linear()` / `.srgb()`)

Linear (default): sRGB u8 -> linear f32 -> resize -> sRGB u8. Correct on gradients, avoids darkening halos. Uses f32 intermediate buffers.
sRGB: Resize directly in gamma space. Uses an i16 integer pipeline with 14-bit fixed-point weights for 4-channel formats. Faster; slightly incorrect on gradients; good enough for thumbnails.

Filters

31 filters covering a range of sharpness/smoothness tradeoffs:

Filter	Category	Window	Notes
`Lanczos`	Sinc	3.0	Sharp, some ringing. Good for photos.
`Lanczos2`	Sinc	2.0	Less ringing than Lanczos-3.
`Robidoux`	Cubic	2.0	Default. Balanced sharpness/smoothness.
`RobidouxSharp`	Cubic	2.0	More detail, slight ringing.
`Mitchell`	Cubic	2.0	Mitchell-Netravali (B=1/3, C=1/3). Balanced blur/ringing.
`CatmullRom`	Cubic	2.0	Catmull-Rom spline (B=0, C=0.5).
`Ginseng`	Jinc-sinc	3.0	Jinc-windowed sinc. Excellent for upscaling.
`Hermite`	Cubic	1.0	Smooth interpolation.
`CubicBSpline`	Cubic	2.0	Very smooth, blurs. B-spline (B=1, C=0).
`Triangle`	Linear	1.0	Bilinear interpolation.
`Box`	Nearest	0.5	Nearest neighbor. Fastest, blocky.
`Fastest`	Cubic	0.74	Minimal quality, maximum speed.

Plus LanczosSharp, Lanczos2Sharp, RobidouxFast, GinsengSharp, CubicFast, Cubic, CubicSharp, CatmullRomFast, CatmullRomFastSharp, MitchellFast, NCubic, NCubicSharp, RawLanczos2, RawLanczos2Sharp, RawLanczos3, RawLanczos3Sharp, Jinc, Linear, LegacyIDCTFilter.

Sharp variants use a slightly reduced blur factor for tighter kernels. Fast variants use smaller windows.

use zenresize::Filter;

let f = Filter::default();      // Robidoux
let all = Filter::all();        // &[Filter] -- all 31 variants

imgref Integration

Typed wrappers for the imgref + rgb crates. These accept any pixel type implementing ComponentSlice (RGBA, BGRA, etc. from the rgb crate).

use zenresize::{resize_4ch, resize_3ch, resize_gray8};
use zenresize::{ResizeConfig, Filter, PixelDescriptor};
use imgref::ImgVec;
use rgb::RGBA8;

let config = ResizeConfig::builder(0, 0, 0, 0) // dimensions overridden by imgref
    .filter(Filter::Lanczos)
    .build();

// 4-channel: pass a PixelDescriptor to control alpha handling
let output: ImgVec<RGBA8> = resize_4ch(
    img.as_ref(),                   // ImgRef<RGBA8>
    512, 384,                       // output dimensions
    PixelDescriptor::RGBA8_SRGB,
    &config,
);

// 3-channel
let output_rgb: ImgVec<RGB8> = resize_3ch(img_rgb.as_ref(), 512, 384, &config);

// Grayscale
let output_gray: ImgVec<u8> = resize_gray8(img_gray.as_ref(), 512, 384, &config);

The imgref functions override the config's dimensions, formats, and stride. Filter, linear mode, and sharpen are preserved.

Feature Flags

Feature	Default	Description
`std`	yes	Enables std library. Disable for `no_std` + `alloc`.
`layout`	yes	Layout negotiation and pipeline execution via zenlayout.
`avx512`	no	Native AVX-512 V-filter kernel (x86-64 only).
`zennode`	no	Self-documenting node definitions for zennode pipeline integration.
`pretty-safe`	no	Replaces bounds-checked indexing with `get_unchecked` in SIMD kernels where bounds are proven by prior guards. ~17% fewer instructions on x86-64. Introduces `unsafe`; the default build is `#![forbid(unsafe_code)]`.

Benchmarks

The benches/ directory contains 19 benchmark binaries covering throughput, precision, and profiling:

Benchmark	What it measures
`paired_bench`	Interleaved paired comparison against pic-scale, fast_image_resize, resize. Statistical diff with 95% CI.
`resize_bench`	Criterion throughput at 50%, 25%, and 200% scale across image sizes.
`tango_bench`	Regression detection across code changes.
`sweep_bench`	Performance across sizes (64–7680 px) and ratios (12.5%–300%). CSV output.
`precision`	f32/u8 accuracy vs f64 reference and cross-library comparison.
`transfer_bench`	sRGB/BT.709/PQ/HLG transfer function speed vs powf and colorutils-rs.
`planar_bench`	Interleaved vs planar resize strategies at 0.5–24 MP.
`profile_*`	Minimal binaries for callgrind/perf (sRGB, linear, f32, f16, streaming).

cargo bench --bench paired_bench    # quick paired comparison
cargo bench --bench resize_bench    # full criterion suite (HTML reports in target/criterion/)

The bench-simd-competitors feature enables SIMD on pic-scale for fair comparison (off by default, so pic-scale runs scalar-only).

Limitations

No f16 channel type (f32 and u16 cover HDR use cases)
No narrow/video signal range -- full range only
Premultiplied input is incompatible with compositing (unpremultiply first, or the pipeline returns CompositeError::PremultipliedInput)
GrayAlpha and Oklab pixel layouts are not supported

Image tech I maintain


State of the art codecs*	zenjpeg · zenpng · zenwebp · zengif · zenavif (rav1d-safe · zenrav1e · zenavif-parse · zenavif-serialize) · zenjxl (jxl-encoder · zenjxl-decoder) · zentiff · zenbitmaps · heic · zenraw · zenpdf · ultrahdr · mozjpeg-rs · webpx
Compression	zenflate · zenzop
Processing	zenresize · zenfilters · zenquant · zenblend
Metrics	zensim · fast-ssim2 · butteraugli · resamplescope-rs · codec-eval · codec-corpus
Pixel types & color	zenpixels · zenpixels-convert · linear-srgb · garb
Pipeline	zenpipe · zencodec · zencodecs · zenlayout · zennode
ImageResizer	ImageResizer (C#) — 24M+ NuGet downloads across all packages
Imageflow	Image optimization engine (Rust) — .NET · node · go — 9M+ NuGet downloads across all packages
Imageflow Server	The fast, safe image server (Rust+C#) — 552K+ NuGet downloads, deployed by Fortune 500s and major brands

_{* as of 2026}

General Rust awesomeness

archmage · magetypes · enough · whereat · zenbench · cargo-copter

And other projects · GitHub @imazen · GitHub @lilith · lib.rs/~lilith · NuGet (over 30 million downloads / 87 packages)

License

Dual-licensed: AGPL-3.0 or commercial.

I've maintained and developed open-source image server software — and the 40+ library ecosystem it depends on — full-time since 2011. Fifteen years of continual maintenance, backwards compatibility, support, and the (very rare) security patch. That kind of stability requires sustainable funding, and dual-licensing is how we make it work without venture capital or rug-pulls. Support sustainable and secure software; swap patch tuesday for patch leap-year.

Our open-source products

Your options:

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See LICENSE-COMMERCIAL for details.

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BENCHMARKS.txt		BENCHMARKS.txt
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
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Cross.toml		Cross.toml
LICENSE-AGPL3		LICENSE-AGPL3
LICENSE-COMMERCIAL		LICENSE-COMMERCIAL
README.md		README.md
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zenresize

Quick Start

Operations

Features

Resizer

StreamingResize

Zero-copy output

f32 streaming

Compositing

Masking

Source Region (Crop)

Fit Modes (Aspect-Ratio Constraints)

EXIF Orientation

Output Padding

Padding color

Crop + Resize + Pad

ResizeConfig

Defaults

Config fields

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Color space (`.linear()` / `.srgb()`)

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